Strengths & Limitations

Balanced assessment

Strengths

  • High detection speed suitable for real-time applications
  • Good balance between accuracy and computational efficiency
  • Supports detection of multiple object classes simultaneously
  • Open-source with extensive community and research backing
  • Flexible architecture allowing customization and extension

Limitations

  • Accuracy can be lower than more recent two-stage detectors on complex datasets
  • Primarily implemented in Caffe, which is less popular than PyTorch or TensorFlow today
  • Limited support for very small object detection compared to newer models
  • Requires GPU for optimal performance, limiting edge deployment